We consider the problem of distributed pose graph optimization (PGO) that has important applications in multi- robot simultaneous localization and mapping (SLAM). We pro- pose the majorization minimization (MM) method for distributed PGO (MM−PGO) that applies to a broad class of robust loss kernels. The MM−PGO method is guaranteed to converge to first-order critical points under mild conditions. Furthermore, noting that the MM−PGO method is reminiscent of proximal methods, we leverage Nesterov’s method and adopt adaptive restarts to accelerate convergence. The resulting accelerated MM methods for distributed PGO—both with a master node in the network (AMM−PGO∗) and without (AMM−PGO#)— have faster convergence in contrast to the MM−PGO method without sacrificing theoretical guarantees. In particular, the AMM−PGO# method, which needs no master node and is fully decentralized, features a novel adaptive restart scheme and has a rate of convergence comparable to that of the AMM−PGO∗ method using a master node to aggregate information from all the nodes. The efficacy of this work is validated through extensive applications to 2D and 3D SLAM benchmark datasets and comprehensive comparisons against existing state-of-the-art methods, indicating that our MM methods converge faster and result in better solutions to distributed PGO. The code is available at https://github.com/MurpheyLab/DPGO.
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Majorization Minimization Methods for Distributed Pose Graph Optimization with Convergence Guarantees
In this paper, we consider the problem of distributed
pose graph optimization (PGO) that has extensive
applications in multi-robot simultaneous localization and mapping
(SLAM). We propose majorization minimization methods
for distributed PGO and show that our proposed methods are
guaranteed to converge to first-order critical points under mild
conditions. Furthermore, since our proposed methods rely a
proximal operator of distributed PGO, the convergence rate
can be significantly accelerated with Nesterov’s method, and
more importantly, the acceleration induces no compromise of
theoretical guarantees. In addition, we also present accelerated
majorization minimization methods for the distributed chordal
initialization that have a quadratic convergence, which can
be used to compute an initial guess for distributed PGO.
The efficacy of this work is validated through applications
on a number of 2D and 3D SLAM datasets and comparisons
with existing state-of-the-art methods, which indicates that our
proposed methods have faster convergence and result in better
solutions to distributed PGO.
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- Award ID(s):
- 1662233
- PAR ID:
- 10283591
- Date Published:
- Journal Name:
- International Conference on Intelligent Robots and Systems
- Page Range / eLocation ID:
- 5058 to 5065
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/IROS45743.2020.9341063
